Hybrid genetic predictive modeling for finding optimal multipurpose multicomponent therapy

Pavlovskii, Vladislav V.; Derevitskii, Ilia V.; Kovalchuk, Sergey V.

doi:10.1016/j.jocs.2022.101772

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…A step-wise approach was proposed to choose drug combinations for compensating carbohydrate metabolism for T2DM patients [ 176 ]. The main carbohydrate metabolism indicator – glycated hemoglobin, arterial blood pressure, and the lipid profile indicator – low-density lipoprotein cholesterol were used as indicators for the prediction.…”

Section: The Application Of ML and Dl Models For The Management Predi...mentioning

confidence: 99%

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Afsaneh¹,

Sharifdini²,

Ghazzaghi³

et al. 2022

Diabetol Metab Syndr

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Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.

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Section: The Application Of ML and Dl Models For The Management Predi...mentioning

confidence: 99%

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Afsaneh¹,

Sharifdini²,

Ghazzaghi³

et al. 2022

Diabetol Metab Syndr

View full text Add to dashboard Cite

show abstract

“…ML and DL applications are also employed for managing T2MD and its evolutions; for example, a personalized postprandial-targeting diet, relying on an ML algorithm that integrates clinical and microbiome features, was used to predict personal postprandial glucose response, in order to control glycemic and metabolic health in patients with newly diagnosed T2DM [ 15 ]. Again, a stepwise approach, based on the combination of machine learning methods, probability graph models, classical statistical modeling tools, and in-house algorithm, was proposed to select drug combinations for compensating carbohydrate metabolism for T2DM patients [ 16 ]. ML-based predictors derived from baseline HbA1c level, comorbidities, demographic variables, and baseline metformin dosage were exploited for predicting the achievement and also for maintaining HbA1c < 7.0% after one year of metformin treatment [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning as a Support for the Diagnosis of Type 2 Diabetes

Agliata

Giordano

Bardozzo

et al. 2023

IJMS

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Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previous studies examined the application of machine learning techniques for prediction of the pathology, and here an artificial neural network shows very promising results as a possible valuable aid in the management and prevention of diabetes. Additionally, its superior ability for long-term predictions makes it an ideal choice for this field of study. We utilized machine learning methods to uncover previously undiscovered associations between an individual’s health status and the development of type 2 diabetes, with the goal of accurately predicting its onset or determining the individual’s risk level. Our study employed a binary classifier, trained on scratch, to identify potential nonlinear relationships between the onset of type 2 diabetes and a set of parameters obtained from patient measurements. Three datasets were utilized, i.e., the National Center for Health Statistics’ (NHANES) biennial survey, MIMIC-III and MIMIC-IV. These datasets were then combined to create a single dataset with the same number of individuals with and without type 2 diabetes. Since the dataset was balanced, the primary evaluation metric for the model was accuracy. The outcomes of this study were encouraging, with the model achieving accuracy levels of up to 86% and a ROC AUC value of 0.934. Further investigation is needed to improve the reliability of the model by considering multiple measurements from the same patient over time.

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